1 - 22 of 22 results

scTDA / single-cell Topological Data Analysis

Serves for topology-based computational analyses. scTDA realizes temporal studies and unbiased transcriptional regulation studies. It is an unsupervised statistical framework that can characterize transient cellular states. This tool can be used to any biological system responding to inductive cues or environmental perturbations like cellular differentiation processes such as hematopoiesis, the evolution of cancer cells, neurodegeneration, or developmental disorders.


A generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. cellity improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells. To ease usability, we developed an R package, which contains functions to extract all necessary classification features from single-cell gene expression data. The package visualizes outliers, which were initially annotated as high quality. Additionally, it offers the ability to automatically filter out low quality cells by using our previously trained SVM model. This gives the user the flexibility to combine this algorithm with prior annotation to identify deceptive cells, or if no annotation is available, to automatically remove low quality cells. Moreover, the R package is built into the processing pipeline. This enables the user to automatically filter out low quality cells whilst data is being processed. In this way, even inexperienced users can process thousands of cells by using only a single simple command.

SinQC / Single-cell RNA-seq Quality Control

A method and software tool to detect technical artifacts in single-cell RNA-seq (scRNA-seq) samples by integrating both gene expression patterns and data quality information. SinQC assumes that if gene expression outliers are also associated with poor sequencing library quality (poor data quality, e.g., low mapped reads, low mapping rate or low library complexity), then they are more likely to be technical artifacts than to be cells with real biological variation. We apply SinQC to nine different scRNA-seq datasets, and show that SinQC is a useful tool for controlling scRNA-seq data quality.


Makes analysis more broadly accessible to researchers. Granatum is a web browser based scRNAseq analysis pipeline that conveniently walks the users through various steps of scRNA-seq analysis. It has a comprehensive list of modules, including plate merging and batch effect removal, outlier sample removal, gene filtering, gene expression normalization, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction.

MAST / Model-based Analysis of Single-cell Transcriptomics

A flexible statistical framework for the analysis of single-cell RNA sequencing data. MAST is suitable for supervised analyses about differential expression of genes and gene modules, as well as unsupervised analyses of model residuals, to generate hypotheses regarding co-expression of genes. MAST accounts for the bimodality of single-cell data by jointly modeling rates of expression (discrete) and positive mean expression (continuous) values. Information from the discrete and continuous parts is combined to infer changes in expression levels using gene or gene set-based statistics. Because our approach uses a generalized linear framework, it can be used to jointly estimate nuisance variation from biological and technical sources, as well as biological effects of interest.


Allows quality control (QC) and analysis components of parallel single cell transcriptome and epigenome data. Dr.seq is a quality control (QC) and analysis pipeline that provides both multifaceted QC reports and cell clustering results. Parallel single cell transcriptome data generated by different technologies can be transformed to the standard input with contained functions. Using relevant commands, the software can also be used to report quality measurements based on four aspects and can generate detailed analysis results for scATAC-seq and Drop-ChIP datasets.

PIVOT / Platform for Interactive analysis and Visualization Of Transcriptomics data

Allows users to analyze and visualize RNA-Seq data. PIVOT furnishes four mains functionalities (i) a graphical interface that is able to wrap existing open source packages in a single user-interface (ii) multiple tools to manipulate datasets to perform derivation or normalization (iii) a way for allowing the compatibility between inputs and outputs from different analysis modules and, (iv) functions for automatically generate reports, publication-quality figures, and reproducible computations.

MAGIC / Markov Affinity-based Graph Imputation of Cells

Provides a method for imputing missing values, and restoring the structure of the data. After the use of MAGIC, two- and three-dimensional gene interactions are restored. MAGIC is able to impute complex and non-linear shapes of interactions. MAGIC also retains cluster structure, enhances cluster-specific gene interactions and restores trajectories, as demonstrated in mouse retinal bipolar cells, hematopoiesis, and a generated epithelial-to-mesenchymal transition dataset.

SCONE / Single-Cell Overview of Normalized Expression

Assists in implementing and assessing the performance of a range of normalization workflows. SCONE evaluates the performance of each workflow and ranks them by aggregating over a set of performance metrics. It is applicable to different single-cell RNASeq (scRNAseq) protocols including microfluidic, plate, and droplet, methods. It allows researchers to compare a set of default normalizations as well as to include user-defined normalization methods.


A method to correct for cell growth in single-cell transcriptomics data. We derive the probability for the cell growth corrected mRNA transcript number given the measured, cell size dependent mRNA transcript number, based on the assumption that the average number of transcripts in a cell increases proportional to the cell's volume during cell cycle. cgCorrect can be used for both data normalization, and to analyze steady-state distributions used to infer the gene expression mechanism.

SINCERA / SINgle CEll RNA-seq profiling Analysis

A generally applicable analytic pipeline for processing single-cell RNA-seq data from a whole organ or sorted cells. SINCERA provides a panel of analytic tools for users to conduct data filtering, normalization, clustering, cell type identification, and gene signature prediction, transcriptional regulatory network construction and important regulatory node identification. The pipeline enables RNA-seq analysis from heterogeneous single cell preparations after the nucleotide sequence reads are aligned to the genome of interest.


Contains useful tools for the analysis of single-cell gene expression data using the statistical software R. scater places an emphasis on tools for quality control, visualisation and pre-processing of data before further downstream analysis. scater enables the following: (i) automated computation of QC metrics; (ii) transcript quantification from read data with pseudo-alignment; (iii) data format standardisation; (iv) rich visualisations for exploratory analysis; (v) seamless integration into the Bioconductor universe; (vi) simple normalisation methods.

Sharq / Single-cell Hierarchical Assignment of Reads and Quality control

Offers a method for managing 3’- end unique molecular identifiers (UMI)-based protocols. Sharq first removes and sorts low quality reads, maps the cleaned files to a reference genome and then performs a specific assignation that generates gene expression tables. The application is able to deal with UMIs and cell barcodes. It can be used for detecting wells where the amplification reaction failed, or to evaluate which cells contained sufficient material relative to an empty well background.


Visualizes transcriptome (RNA expression) data from hundreds of samples. Flotilla is a Python package. Flotilla is an open source, community-driven software written in Python that enables biologists with rudimentary knowledge of statistical methods and programming to analyze and visualize hundreds of RNA-seq datasets. This package includes interactive functions for common and important tasks in computational analyses of biological datasets such as dimensionality reduction, covariance analysis, classification, regression and outlier detection.